Then, we measure the impact of the different design choices used to implement our model. Specifically: i) different proxy label generation functions (\(d^*_{min}\)  and \(d^*_{avg}\)  for respectively the minimum and the average among the valid depths in the patch), ii) the prediction head (MLP or a five layers decoder with skip connections, where each layer has two \(3\times 3\)  convolutional blocks followed by \(2\times 2\)  nearest-neighbor upsampling) and iii) the distribution function underlying the loss term between Gaussian \(\mathcal {L}_G\) , Laplacian \(\mathcal {L}_L\)  [1]} and the modified version of both \(\mathcal {L}_{G^*}\)  and \(\mathcal {L}_{L^*}\)  as described in Sec. REF .
Table REF  collects results by several variants of our framework on both CV and 142 splits, using a 9\(\times \) 9 window and sampling features at all resolutions following the outcomes from Table REF .
The scores are generally lower on the CV split because of the many missing labels from ground-truth maps obtained semi-automatically, resulting in several outliers being missing in the AUC evaluation.
We can also notice that \(\mathcal {L}_{L^*}\)  and \(\mathcal {L}_{G^*}\)  always outperform their original counterparts, assessing the quality of our formulation. Moreover, the synergy between the minimum proxy label strategy and the MLP yields the best results overall. Finally,
even if both \(\mathcal {L}_{L^*}\)  and \(\mathcal {L}_{G^*}\)  are competitive, we choose \(\mathcal {L}_{G^*}\)  since it leads to the best overall results.
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